The long-term objective of this research project is to identify the computational rules and design features that endow the central nervous system with its remarkable ability to acquire, store and retrieve information. To achieve this goal we will combine cellular and computational approaches in the analysis of information processing in a identified neural network which underlies a well analyzed behavioral response, the siphon withdrawal reflex (SWR), in the marine molluse Aplysia. This simple reflex offers two key advantages for a computational analysis of learning: (1) the reflex exhibits a variety of forms of both non-associative and associative learning; and (2) the neural circuit underlying the reflex is quite well understood. Thus this system provides an excellent opportunity to construct a biologically realistic quantitative model, based on empirically determined cellular properties and synaptic interactions, that can provide important insights into the nature of information process underlying learning and memory. There are three specific aims of the project: (1) a CELLULAR ANALYSIS will be aimed at examining the mechanisms of diverse forms of plasticity exhibited by individual, identified circuit elements: single cells and synapses; (2) a CIRCUIT ANALYSIS will be aimed at combining the elements studied at the first level into a functional network analysis, examining how the types of plasticity identified at individual circuit elements interact and contribute to both short-term and long-term plasticity exhibited within the SWR network; (3) APPLICATIONS OF THE MODEL will be aimed at examining how the different forms of cellular and network plasticity we have identified at the first two levels can account for actual behavioral instances of learning and memory in the SWR. Information obtained from this project would be of significance (1) from a basic scientific perspective, providing valuable insights into the cellular mechanisms underlying behavior and behavioral plasticity; (2) from an applied perspective, elucidating general computational principles utilized by a network to generate adaptive modifications such as signal optimizations and learning (which in turn could facilitate construction of interactive machines capable of goal-directed behavior, for application in both clinical and educational contexts) and (3) from a theoretical perspective providing information concerning the degree to which adaptive modifications required for learning within a neural circuit can be accounted for by cellular changes at specific loci on the one hand, or by distributed network processes on the other.